We consider a partially observable environment where \(n\)  agents cooperate to solve a task.
As shown in Figure REF , each agent \(i\)  receives a local observation \(o^t_i\)  at time step \(t\) .
The local observation is encoded into a observation feature \(h^t_i\)  by a message encoder (M-ENC) and a dependency feature \(g^t_i\) 
by a dependency encoder (D-ENC), respectively.
The intention for each agent \(i\)  is \(\widetilde{u}_i^t=f(h^t_i)\) .
The dependency matrix \(w^d\) , which represents the interdependency between agents, is obtained according to \(g^t_i\)  and \(\widetilde{u}_i^t\) .
Based on \(w^d\) , the hierarchical relationship \(w^f\)  is built through the leader-follower forest generator.
During communication, agents share their intentions followed the order based on \(w^f\) , i.e., from the root node to the leaf node.
To be detailed, each agent \(i\)  makes the decision \(u^t_i=\pi (h^t_i,c^t_i,x^t_i) \)  based on its leaders' intentions, and then
send it to its followers,
where \(c^t_i\)  and \(x^t_i\)  denote the aggregated observation and intention messages from other agents, respectively.
Finally, agents perform joint actions \(U=(u^t_1,...,u^t_n)\)  and receive a global reward \(r^t\)  from the environment.
The agent network is trained by linear value decomposition [1]} according to \(r^t\) .
In addition, we model the task of finding out the optimal observation-dependent leader-follower forest as a reinforcement learning problem,
where the action space is defined as the dependency matrix \(w^d\) .
The forest is trained by DDPG [2]} based on a communication reward \(r^t_{c}\) , which reflects the improvement of the decisions.
The pseudo-code is shown in Appendix.
In IS-LFF, the intentions flow from the root nodes to the leaf nodes, allowing agents to receive intentions from the parent node and their ancestors.
Consequently, the forest makes full use of information and guarantees one-sided intention sharing.
